Keun-Hwa Jung1,2, Kimberly A Stephens1, Kathryn M Yochim1, Joost M Riphagen1,3, Chan Mi Kim1, Randy L Buckner4,5,6, David H Salat1,7. 1. Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston (K.-H.J., K.A.S., K.M.Y., J.M.R., C.M.K., D.H.S.). 2. Department of Neurology, Seoul National University Hospital, Republic of Korea (K.-H.J.). 3. Alzheimer Center Limburg, School for Mental Health and Neuroscience (MHeNS), Maastricht University Medical Centre, the Netherlands (J.M.R.). 4. Department of Psychology (R.L.B.), Harvard University, Cambridge. 5. Center for Brain Science (R.L.B.), Harvard University, Cambridge. 6. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston (R.L.B.). 7. VA Boston Healthcare System, Neuroimaging Research for Veterans Center, MA (D.S.H.).
Abstract
BACKGROUND AND PURPOSE: Cerebral white matter signal abnormalities (WMSAs) are a significant radiological marker associated with brain and vascular aging. However, understanding their clinical impact is limited because of their pathobiological heterogeneity. We determined whether use of robust reliable automated procedures can distinguish WMSA classes with different clinical consequences. METHODS: Data from generally healthy participants aged >50 years with moderate or greater WMSA were selected from the Human Connectome Project-Aging (n=130). WMSAs were segmented on T1 imaging. Features extracted from WMSA included total and regional volume, number of discontinuous clusters, size of noncontiguous lesion, contrast of lesion intensity relative to surrounding normal appearing tissue using a fully automated procedure. Hierarchical clustering was used to classify individuals into distinct classes of WMSA. Radiological and clinical variability was evaluated across the individual WMSA classes. RESULTS: Class I was characterized by multiple, small, lower-contrast lesions predominantly in the deep WM; class II by large, confluent lesions in the periventricular WM; and class III by higher-contrast lesions restricted to the juxtaventricular WM. Class II was associated with lower myelin content than the other 2 classes. Class II was more prevalent in older subjects and was associated with a higher prevalence of hypertension and lower physical activity levels. Poor sleep quality was associated with a greater risk of class I. CONCLUSIONS: We classified heterogeneous subsets of cerebral white matter lesions into distinct classes that have different clinical risk factors. This new method for identifying classes of WMSA will be important in understanding the underlying pathophysiology and in determining the impact on clinical outcomes.
BACKGROUND AND PURPOSE: Cerebral white matter signal abnormalities (WMSAs) are a significant radiological marker associated with brain and vascular aging. However, understanding their clinical impact is limited because of their pathobiological heterogeneity. We determined whether use of robust reliable automated procedures can distinguish WMSA classes with different clinical consequences. METHODS: Data from generally healthy participants aged >50 years with moderate or greater WMSA were selected from the Human Connectome Project-Aging (n=130). WMSAs were segmented on T1 imaging. Features extracted from WMSA included total and regional volume, number of discontinuous clusters, size of noncontiguous lesion, contrast of lesion intensity relative to surrounding normal appearing tissue using a fully automated procedure. Hierarchical clustering was used to classify individuals into distinct classes of WMSA. Radiological and clinical variability was evaluated across the individual WMSA classes. RESULTS: Class I was characterized by multiple, small, lower-contrast lesions predominantly in the deep WM; class II by large, confluent lesions in the periventricular WM; and class III by higher-contrast lesions restricted to the juxtaventricular WM. Class II was associated with lower myelin content than the other 2 classes. Class II was more prevalent in older subjects and was associated with a higher prevalence of hypertension and lower physical activity levels. Poor sleep quality was associated with a greater risk of class I. CONCLUSIONS: We classified heterogeneous subsets of cerebral white matter lesions into distinct classes that have different clinical risk factors. This new method for identifying classes of WMSA will be important in understanding the underlying pathophysiology and in determining the impact on clinical outcomes.
Entities:
Keywords:
brain; hypertension; risk factors; sleep; white matter
Authors: Christian Enzinger; Stephen Smith; Franz Fazekas; Gunther Drevin; Stefan Ropele; Thomas Nichols; Timothy Behrens; Reinhold Schmidt; Paul M Matthews Journal: J Neurol Date: 2006-04-10 Impact factor: 4.849
Authors: Paul A Nyquist; Murat Bilgel; Rebecca Gottesman; Lisa R Yanek; Taryn F Moy; Lewis C Becker; Jennifer L Cuzzocreo; Jerry Prince; Bruce A Wasserman; David M Yousem; Diane M Becker; Brian G Kral; Dhananjay Vaidya Journal: Neurobiol Aging Date: 2015-01-07 Impact factor: 4.673
Authors: Susan Y Bookheimer; David H Salat; Melissa Terpstra; Beau M Ances; Deanna M Barch; Randy L Buckner; Gregory C Burgess; Sandra W Curtiss; Mirella Diaz-Santos; Jennifer Stine Elam; Bruce Fischl; Douglas N Greve; Hannah A Hagy; Michael P Harms; Olivia M Hatch; Trey Hedden; Cynthia Hodge; Kevin C Japardi; Taylor P Kuhn; Timothy K Ly; Stephen M Smith; Leah H Somerville; Kâmil Uğurbil; Andre van der Kouwe; David Van Essen; Roger P Woods; Essa Yacoub Journal: Neuroimage Date: 2018-10-15 Impact factor: 6.556
Authors: Nina E Fultz; Giorgio Bonmassar; Kawin Setsompop; Robert A Stickgold; Bruce R Rosen; Jonathan R Polimeni; Laura D Lewis Journal: Science Date: 2019-11-01 Impact factor: 47.728
Authors: Elizabeth C Leritz; Juli Shepel; Victoria J Williams; Lewis A Lipsitz; Regina E McGlinchey; William P Milberg; David H Salat Journal: Hum Brain Mapp Date: 2013-01-30 Impact factor: 5.038
Authors: Maria-Eleni Dounavi; Audrey Low; Graciela Muniz-Terrera; Karen Ritchie; Craig W Ritchie; Li Su; Hugh S Markus; John T O'Brien Journal: Brain Commun Date: 2022-05-05